Internal Collusive Eavesdropping of Interference Alignment Networks
Bibliographic record
Abstract
Interference alignment (IA) networks seem secure, due to the fact that signals from the legitimate network may act as interference to disrupt the external eavesdropping. However, when some users inside the network are cooperating to eavesdrop one certain user, it will not be secure any longer. Thus, we concentrate on the eavesdropping attacks in this paper, and propose a novel collusive eavesdropping scheme (CES) in a K- user IA network, where one of the users is eavesdropped by an eavesdropper with the aid of the other (K - 2) cooperators. To perform the passive eavesdropping without being noticed by the targeted user, the precoding and decoding matrices of the eavesdropper and cooperators are re-designed, and some of the cooperators should sacrifice their own quality of transmission to help the eavesdropper meet the feasibility condition. Extensive simulation results are provided to show the eavesdropping effectiveness of the proposed CES in IA networks.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".